Search Results for "python framework"
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Showing 52 open source projects for "python framework"
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Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
Datumaro is a flexible Python-based dataset management framework and command-line tool for building, analyzing, transforming, and converting computer vision datasets in many popular formats. It supports importing and exporting annotations and images across a wide variety of standards like COCO, PASCAL VOC, YOLO, ImageNet, Cityscapes, and many more, enabling easy integration with different training pipelines and tools. -
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. ... -
4
Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. ... -
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5
Pathway
Python ETL framework for stream processing, real-time analytics, LLM
...Unlike traditional batch processing frameworks, Pathway continuously updates the results of your data logic as new events arrive, functioning more like a database that reacts in real-time. It supports Python, integrates with modern data tools, and offers a deterministic dataflow model to ensure reproducibility and correctness. -
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Mercury
Convert Python notebook to web app and share with non-technical users
Turn Python notebooks to web applications with open-source Mercury framework. Hide code and add interactive widgets. Non-technical users can tweak widgets and execute notebook with new parameters. The core of Mercury is Open Source under AGPLv3. We provide Mercury Pro with additional features, dedicated support and friendly commercial license. -
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Dash
Build beautiful web-based analytic apps, no JavaScript required
Dash is a Python framework for building beautiful analytical web applications without any JavaScript. Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. ... -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. ... -
11
nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
...It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines. nb-clean can also be used as a pre-commit hook. You may prefer this to the Git filter if your project already uses the pre-commit framework. Note that the Git filter and pre-commit hook work differently, with different effects on your working directory. ... -
12
gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is... -
13
SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
14
NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA... -
15
Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components.... -
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Avogadro
An intuitive molecular editor and visualization tool
Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science and related areas. It offers a flexible rendering framework and a powerful plugin architecture. -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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OpenPTV
OpenPTV
OpenPTV is an abbreviation of the open source particle tracking velocimetry. This is an open source framework, developed jointly by academic institutions, that deals primarily with the three-dimensional particle tracking experiments in complex and turbulent flows. More about 3D-PTV on Wikipedia and links from our website: http://www.openptv.net -
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Muse: Middleware Universal Scripting idE
Automate: WebSphere; WebLogic; JBoss; Glassfish; Tomcat; Linux, WinRM
...Win x64, Linux WINE x64. Built-In JVM. Java 8/9/10, Amazon Corretto, JETPack13/14/16, IBM SDK Compatible. *** Now with powerful JBoss / GlassFish / Tomcat / Linux Active Auditing Framework. Tomcat / Glassfish 2 Python - Configuration Snapshots *** Infrastructure-as-Code, Code-Writing-Code Designed to Run on JETPack: https://sourceforge.net/projects/jetpack Muse.2026.03.x - Win 10 / Win11 / Linux (WINE) Muse.2025.12.x - Win8 / Win 10 / Win11 / Linux (WINE) -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. ... -
21
dbt-re-data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlaying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Data transformations in re_data are implemented and exposed as models & macros in this dbt package. Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up... -
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Mara Pipelines
A lightweight opinionated ETL framework, halfway between plain scripts
This package contains a lightweight data transformation framework with a focus on transparency and complexity reduction. Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code. PostgreSQL as a data processing engine. Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines. GNU make semantics. Nodes depend on the completion of upstream nodes. -
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SQLBucket
Lightweight library to write, orchestrate and test your SQL ETL
SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. -
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
...Rules are written in Python; they can utilize any Python libraries or functions. Merge similar alerts and automatically promote new rules if they are not too noisy. Ingested logs and generated alerts can be retroactively searched for compliance and research. Serverless design is cheaper, easier to maintain, and scales to terabytes per day. Deployment is automated, simple, safe and repeatable for any AWS account. -
25
MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,...
